The Simpsons Characters identification

Group 17

• Chen, Kun - 8977010
• Ku, Shih-Chieh - 8906826

Objective

---

The objective of this project is to develop a deep learning model capable of accurately classify the different characters from the popular animated television series "The Simpsons" based on images.

Motivation

---

The Simpsons is one of the longest-running and most beloved animated series, featuring a vast and diverse array of characters. Predicting these characters from images is not only a fun and engaging task but also holds practical applications in media analysis, entertainment, and fan engagement.

Moreover, The Simpsons' style tends to use flat colors and simplistic character compositions, unlike realistic images which incorporate shadows, angles, profiles, and shapes. Given these differences, we are interested in determining whether training a model on The Simpsons' data would be more challenging or if achieving good performance would be relatively easier.

The number of data set (Data set link (Kaggle)):

• 20933 (images)
• 24395 (images), after data augmentation

Referenced code:

• Simpsons Image Classification -CNN | Val_acc=93%
• Recognize the Simpsons TensorFlow (88% acc.)
• CNN + ResNet

References:

• Keras Applications

The pre-trained models used:

• ResNet50
• MobileNetV2

import os
import tensorflow as tf
import keras
import matplotlib.image as mpimg
import numpy as np
import random
import seaborn as sns
import matplotlib.pyplot as plt 
from keras.models import Sequential 
from keras.layers import Dropout, Flatten, Dense, Conv2D, MaxPooling2D, Flatten, Dense
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.preprocessing import image
from PIL import Image
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import confusion_matrix, classification_report

import splitfolders

input_dir = "./input/the-simpsons-characters-dataset/"
output_dir = "./data"

splitfolders.ratio(input_dir, output=output_dir, seed=66, ratio=(0.8, 0.1, 0.1))

Copying files: 20933 files [00:25, 817.21 files/s] 

train_dir = "./data/train"
val_dir = "./data/val"
test_dir = "./data/test"

image_size = (180, 180, 3)
# number of epochs to train top model 
epochs = 50 #this has been changed after multiple model run 
# batch size used by flow_from_directory and predict_generator 
batch_size = 100

labels = os.listdir(train_dir)

train_datagen = ImageDataGenerator(
    rescale=1.0 / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range=0.2,
)

test_datagen = ImageDataGenerator(rescale=1.0 / 255)

training_set = train_datagen.flow_from_directory(
    train_dir,
    target_size=image_size[:2],
    batch_size=batch_size,
    class_mode="categorical",
    color_mode="rgb",
)

val_set = test_datagen.flow_from_directory(
    val_dir,
    target_size=image_size[:2],
    batch_size=batch_size,
    class_mode="categorical",
    color_mode="rgb",
)

test_set = test_datagen.flow_from_directory(
    test_dir,
    target_size=image_size[:2],
    batch_size=batch_size,
    class_mode="categorical",
    color_mode="rgb",
    shuffle=False
)

Found 16727 images belonging to 42 classes.
Found 2073 images belonging to 42 classes.
Found 2133 images belonging to 42 classes.

img_path = "./data/train/homer_simpson/pic_0028.jpg"
img = image.load_img(img_path, target_size=(1080, 1080, 3))
plt.imshow(img)
plt.show()

Display each classes with one image

classes = list(training_set.class_indices.keys())
train_dir = './data/train'

def plot_image(size):
    plt.figure(figsize=(15, 18))
    for i, class_1 in enumerate(classes):

        folder_path = f"{train_dir}/{class_1}"
        files = os.listdir(folder_path)
        files = [f for f in files if os.path.isfile(os.path.join(folder_path, f))]
        random_file = random.choice(files)

        plt.subplot(size, size, i + 1)
        img = mpimg.imread(f"{folder_path}/{random_file}")
        plt.imshow(img)
        plt.axis("off")
        plt.title(' '.join(class_1.split("_")))
    plt.tight_layout()
    plt.show()


plot_image(7)

Showing total number of each class (Imbalanced dataset)

def plot_size(len_l):
    plt.figure(figsize=(15, 15))
    ax = sns.barplot(x=len_l, y=classes, orient="h", color="navy")
    ax.set_xticks(np.arange(0, 2000, 2000))
    ax.set_xlabel("Number of Images")
    ax.set_ylabel("Classes")
    ax.set_title("Number of samples for each class", fontsize=20)
    for i, p in enumerate(ax.patches):
        ax.text(
            p.get_width(),
            p.get_y() + p.get_height() / 2.0,
            "{}".format(len_l[i]),
            va="center",
            fontsize=15,
        )

len_list = []

for i, class_ in enumerate(classes):
    len_list.append(len(os.listdir(f"{train_dir}/{class_}")))
        
plot_size(len_list)

Data Augmentation, for the class which has lower 100 images add 9 times the number of origin images

def augment_image(image):
    image = tf.image.random_flip_left_right(image)
    # image = tf.image.random_flip_up_down(image)
    image = tf.image.random_brightness(image, max_delta=0.2)
    image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
    # image = tf.image.rot90(image, k=tf.random.uniform(shape=[], minval=0, maxval=4, dtype=tf.int32))
    
    return image

for i, class_ in enumerate(classes):
    if len_list[i] < 100:
        folder_path = f"{train_dir}/{class_}"
        files = os.listdir(folder_path)
        seq = len_list[i]
        for file_ in files:
            for i in range(9):
                img = mpimg.imread(f"{folder_path}/{file_}")
                
                image = tf.io.read_file(f"{folder_path}/{file_}")
                image = tf.image.decode_jpeg(image, channels=3)
                augmented_image = augment_image(image)
                img = Image.fromarray(augmented_image.numpy())
                padded_number = str(seq).zfill(4)
                img.save(os.path.join(folder_path, f"pic_{padded_number}.png"))
                seq += 1

len_list_ = []

for i, class_ in enumerate(classes):
    len_list_.append(len(os.listdir(f"{train_dir}/{class_}")))
        
plot_size(len_list_)

training_set = train_datagen.flow_from_directory(
    train_dir,
    target_size=image_size[:2],
    batch_size=batch_size,
    class_mode="categorical",
    color_mode="rgb",
)

Found 24395 images belonging to 42 classes.

Customeized CNN model

The following is referenced from Simpsons Image Classification -CNN | Val_acc=93%

model_cus = Sequential()
model_cus.add(Conv2D(32, (3, 3), padding="same", input_shape=image_size, activation="relu"))
model_cus.add(Conv2D(32, (3, 3), activation="relu"))
model_cus.add(MaxPooling2D(pool_size=(2, 2)))
model_cus.add(Dropout(0.2))

model_cus.add(Conv2D(64, (3, 3), padding="same", activation="relu"))
model_cus.add(Conv2D(64, (3, 3), activation="relu"))
model_cus.add(MaxPooling2D(pool_size=(2, 2)))
model_cus.add(Dropout(0.2))

model_cus.add(Conv2D(256, (3, 3), padding="same", activation="relu"))
model_cus.add(Conv2D(256, (3, 3), activation="relu"))
model_cus.add(MaxPooling2D(pool_size=(2, 2)))
model_cus.add(Dropout(0.2))

model_cus.add(Flatten())
model_cus.add(Dense(1024, activation="relu"))
model_cus.add(Dropout(0.5))
model_cus.add(Dense(len(labels), activation="softmax"))

model_cus.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

model_cus.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 180, 180, 32)      896       
                                                                 
 conv2d_1 (Conv2D)           (None, 178, 178, 32)      9248      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 89, 89, 32)       0         
 )                                                               
                                                                 
 dropout (Dropout)           (None, 89, 89, 32)        0         
                                                                 
 conv2d_2 (Conv2D)           (None, 89, 89, 64)        18496     
                                                                 
 conv2d_3 (Conv2D)           (None, 87, 87, 64)        36928     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 43, 43, 64)       0         
 2D)                                                             
                                                                 
 dropout_1 (Dropout)         (None, 43, 43, 64)        0         
                                                                 
 conv2d_4 (Conv2D)           (None, 43, 43, 256)       147712    
                                                                 
 conv2d_5 (Conv2D)           (None, 41, 41, 256)       590080    
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 20, 20, 256)      0         
 2D)                                                             
                                                                 
 dropout_2 (Dropout)         (None, 20, 20, 256)       0         
                                                                 
 flatten (Flatten)           (None, 102400)            0         
                                                                 
 dense (Dense)               (None, 1024)              104858624 
                                                                 
 dropout_3 (Dropout)         (None, 1024)              0         
                                                                 
 dense_1 (Dense)             (None, 42)                43050     
                                                                 
=================================================================
Total params: 105,705,034
Trainable params: 105,705,034
Non-trainable params: 0
_________________________________________________________________

checkpoint_filepath_cus = "./models/cus.h5"

model_cus_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
    filepath=checkpoint_filepath_cus,
    save_weights_only=False,
    monitor="val_accuracy",
    mode="max",
    save_best_only=True,
)

callbacks = [
    EarlyStopping(patience=5, monitor="val_accuracy", mode="max"),
    model_cus_checkpoint_callback,
]

history_customized = model_cus.fit(
    training_set, epochs=epochs, validation_data=val_set, callbacks=callbacks
)

Epoch 1/50
244/244 [==============================] - 253s 967ms/step - loss: 3.5161 - accuracy: 0.0810 - val_loss: 2.9515 - val_accuracy: 0.2262
Epoch 2/50
244/244 [==============================] - 111s 451ms/step - loss: 3.0078 - accuracy: 0.1891 - val_loss: 2.3095 - val_accuracy: 0.3739
Epoch 3/50
244/244 [==============================] - 111s 451ms/step - loss: 2.5875 - accuracy: 0.2971 - val_loss: 1.8757 - val_accuracy: 0.5089
Epoch 4/50
244/244 [==============================] - 110s 449ms/step - loss: 2.2122 - accuracy: 0.3975 - val_loss: 1.4827 - val_accuracy: 0.5909
Epoch 5/50
244/244 [==============================] - 110s 450ms/step - loss: 1.8888 - accuracy: 0.4776 - val_loss: 1.2111 - val_accuracy: 0.6585
Epoch 6/50
244/244 [==============================] - 110s 449ms/step - loss: 1.6264 - accuracy: 0.5424 - val_loss: 0.9674 - val_accuracy: 0.7236
Epoch 7/50
244/244 [==============================] - 110s 451ms/step - loss: 1.4421 - accuracy: 0.5960 - val_loss: 0.9006 - val_accuracy: 0.7472
Epoch 8/50
244/244 [==============================] - 111s 452ms/step - loss: 1.3089 - accuracy: 0.6338 - val_loss: 0.7888 - val_accuracy: 0.7868
Epoch 9/50
244/244 [==============================] - 111s 452ms/step - loss: 1.1793 - accuracy: 0.6723 - val_loss: 0.7723 - val_accuracy: 0.8003
Epoch 10/50
244/244 [==============================] - 108s 441ms/step - loss: 1.0857 - accuracy: 0.6912 - val_loss: 0.7476 - val_accuracy: 0.7974
Epoch 11/50
244/244 [==============================] - 110s 451ms/step - loss: 1.0126 - accuracy: 0.7176 - val_loss: 0.6013 - val_accuracy: 0.8249
Epoch 12/50
244/244 [==============================] - 111s 452ms/step - loss: 0.9694 - accuracy: 0.7272 - val_loss: 0.5753 - val_accuracy: 0.8452
Epoch 13/50
244/244 [==============================] - 111s 451ms/step - loss: 0.8851 - accuracy: 0.7547 - val_loss: 0.5176 - val_accuracy: 0.8543
Epoch 14/50
244/244 [==============================] - 111s 452ms/step - loss: 0.8271 - accuracy: 0.7679 - val_loss: 0.5140 - val_accuracy: 0.8640
Epoch 15/50
244/244 [==============================] - 111s 455ms/step - loss: 0.7942 - accuracy: 0.7784 - val_loss: 0.4868 - val_accuracy: 0.8673
Epoch 16/50
244/244 [==============================] - 111s 452ms/step - loss: 0.7446 - accuracy: 0.7925 - val_loss: 0.4451 - val_accuracy: 0.8799
Epoch 17/50
244/244 [==============================] - 108s 441ms/step - loss: 0.7371 - accuracy: 0.7955 - val_loss: 0.4221 - val_accuracy: 0.8799
Epoch 18/50
244/244 [==============================] - 111s 453ms/step - loss: 0.6932 - accuracy: 0.8059 - val_loss: 0.4021 - val_accuracy: 0.8910
Epoch 19/50
244/244 [==============================] - 111s 452ms/step - loss: 0.6523 - accuracy: 0.8150 - val_loss: 0.4047 - val_accuracy: 0.8915
Epoch 20/50
244/244 [==============================] - 108s 440ms/step - loss: 0.6475 - accuracy: 0.8170 - val_loss: 0.4121 - val_accuracy: 0.8900
Epoch 21/50
244/244 [==============================] - 111s 453ms/step - loss: 0.6202 - accuracy: 0.8271 - val_loss: 0.3896 - val_accuracy: 0.9006
Epoch 22/50
244/244 [==============================] - 108s 440ms/step - loss: 0.5927 - accuracy: 0.8335 - val_loss: 0.4148 - val_accuracy: 0.8953
Epoch 23/50
244/244 [==============================] - 110s 451ms/step - loss: 0.5646 - accuracy: 0.8394 - val_loss: 0.3565 - val_accuracy: 0.9011
Epoch 24/50
244/244 [==============================] - 112s 457ms/step - loss: 0.5653 - accuracy: 0.8414 - val_loss: 0.3231 - val_accuracy: 0.9074
Epoch 25/50
244/244 [==============================] - 108s 439ms/step - loss: 0.5370 - accuracy: 0.8479 - val_loss: 0.3804 - val_accuracy: 0.9055
Epoch 26/50
244/244 [==============================] - 111s 452ms/step - loss: 0.5195 - accuracy: 0.8517 - val_loss: 0.3456 - val_accuracy: 0.9112
Epoch 27/50
244/244 [==============================] - 108s 442ms/step - loss: 0.5034 - accuracy: 0.8591 - val_loss: 0.3566 - val_accuracy: 0.9011
Epoch 28/50
244/244 [==============================] - 112s 456ms/step - loss: 0.5079 - accuracy: 0.8561 - val_loss: 0.3174 - val_accuracy: 0.9137
Epoch 29/50
244/244 [==============================] - 108s 442ms/step - loss: 0.5035 - accuracy: 0.8583 - val_loss: 0.3546 - val_accuracy: 0.9026
Epoch 30/50
244/244 [==============================] - 108s 443ms/step - loss: 0.4676 - accuracy: 0.8668 - val_loss: 0.3161 - val_accuracy: 0.9122
Epoch 31/50
244/244 [==============================] - 112s 457ms/step - loss: 0.4546 - accuracy: 0.8722 - val_loss: 0.3156 - val_accuracy: 0.9146
Epoch 32/50
244/244 [==============================] - 110s 449ms/step - loss: 0.4815 - accuracy: 0.8641 - val_loss: 0.3149 - val_accuracy: 0.9161
Epoch 33/50
244/244 [==============================] - 108s 440ms/step - loss: 0.4420 - accuracy: 0.8762 - val_loss: 0.3658 - val_accuracy: 0.9055
Epoch 34/50
244/244 [==============================] - 108s 440ms/step - loss: 0.4534 - accuracy: 0.8720 - val_loss: 0.3704 - val_accuracy: 0.9122
Epoch 35/50
244/244 [==============================] - 109s 444ms/step - loss: 0.4304 - accuracy: 0.8778 - val_loss: 0.3176 - val_accuracy: 0.9161
Epoch 36/50
244/244 [==============================] - 118s 483ms/step - loss: 0.4246 - accuracy: 0.8789 - val_loss: 0.2930 - val_accuracy: 0.9228
Epoch 37/50
244/244 [==============================] - 110s 448ms/step - loss: 0.4324 - accuracy: 0.8771 - val_loss: 0.2906 - val_accuracy: 0.9257
Epoch 38/50
244/244 [==============================] - 107s 437ms/step - loss: 0.4279 - accuracy: 0.8802 - val_loss: 0.2919 - val_accuracy: 0.9204
Epoch 39/50
244/244 [==============================] - 108s 443ms/step - loss: 0.4302 - accuracy: 0.8785 - val_loss: 0.3516 - val_accuracy: 0.9059
Epoch 40/50
244/244 [==============================] - 108s 442ms/step - loss: 0.4020 - accuracy: 0.8874 - val_loss: 0.3033 - val_accuracy: 0.9209
Epoch 41/50
244/244 [==============================] - 108s 440ms/step - loss: 0.4164 - accuracy: 0.8831 - val_loss: 0.2951 - val_accuracy: 0.9194
Epoch 42/50
244/244 [==============================] - 111s 454ms/step - loss: 0.3968 - accuracy: 0.8892 - val_loss: 0.2789 - val_accuracy: 0.9296
Epoch 43/50
244/244 [==============================] - 107s 438ms/step - loss: 0.4069 - accuracy: 0.8845 - val_loss: 0.2901 - val_accuracy: 0.9257
Epoch 44/50
244/244 [==============================] - 109s 446ms/step - loss: 0.3912 - accuracy: 0.8913 - val_loss: 0.2981 - val_accuracy: 0.9320
Epoch 45/50
244/244 [==============================] - 108s 440ms/step - loss: 0.3743 - accuracy: 0.8937 - val_loss: 0.2791 - val_accuracy: 0.9301
Epoch 46/50
244/244 [==============================] - 107s 438ms/step - loss: 0.3811 - accuracy: 0.8919 - val_loss: 0.2658 - val_accuracy: 0.9305
Epoch 47/50
244/244 [==============================] - 107s 439ms/step - loss: 0.3773 - accuracy: 0.8949 - val_loss: 0.2787 - val_accuracy: 0.9281
Epoch 48/50
244/244 [==============================] - 108s 441ms/step - loss: 0.3742 - accuracy: 0.8958 - val_loss: 0.2779 - val_accuracy: 0.9257
Epoch 49/50
244/244 [==============================] - 108s 443ms/step - loss: 0.3736 - accuracy: 0.8965 - val_loss: 0.2718 - val_accuracy: 0.9276

Trends for accuracy and loss: training dataset versus validation set

Load highest val-accuracy model

best_from_custome_customized_model = keras.models.load_model(checkpoint_filepath_cus)

Table below is referenced from Keras (https://keras.io/api/applications/)

Model	Size (MB)	Top-1 Accuracy	Top-5 Accuracy	Parameters	Depth	Time (ms) per inference step (CPU)	Time (ms) per inference step (GPU)
Xception	88	79.0%	94.5%	22.9M	81	109.4	8.1
VGG16	528	71.3%	90.1%	138.4M	16	69.5	4.2
VGG19	549	71.3%	90.0%	143.7M	19	84.8	4.4
ResNet50	98	74.9%	92.1%	25.6M	107	58.2	4.6
ResNet50V2	98	76.0%	93.0%	25.6M	103	45.6	4.4
ResNet101	171	76.4%	92.8%	44.7M	209	89.6	5.2
ResNet101V2	171	77.2%	93.8%	44.7M	205	72.7	5.4
ResNet152	232	76.6%	93.1%	60.4M	311	127.4	6.5
ResNet152V2	232	78.0%	94.2%	60.4M	307	107.5	6.6
InceptionV3	92	77.9%	93.7%	23.9M	189	42.2	6.9
InceptionResNetV2	215	80.3%	95.3%	55.9M	449	130.2	10.0
MobileNet	16	70.4%	89.5%	4.3M	55	22.6	3.4
MobileNetV2	14	71.3%	90.1%	3.5M	105	25.9	3.8
DenseNet121	33	75.0%	92.3%	8.1M	242	77.1	5.4
DenseNet169	57	76.2%	93.2%	14.3M	338	96.4	6.3
DenseNet201	80	77.3%	93.6%	20.2M	402	127.2	6.7
NASNetMobile	23	74.4%	91.9%	5.3M	389	27.0	6.7
NASNetLarge	343	82.5%	96.0%	88.9M	533	344.5	20.0
EfficientNetB0	29	77.1%	93.3%	5.3M	132	46.0	4.9
EfficientNetB1	31	79.1%	94.4%	7.9M	186	60.2	5.6
EfficientNetB2	36	80.1%	94.9%	9.2M	186	80.8	6.5
EfficientNetB3	48	81.6%	95.7%	12.3M	210	140.0	8.8
EfficientNetB4	75	82.9%	96.4%	19.5M	258	308.3	15.1
EfficientNetB5	118	83.6%	96.7%	30.6M	312	579.2	25.3
EfficientNetB6	166	84.0%	96.8%	43.3M	360	958.1	40.4
EfficientNetB7	256	84.3%	97.0%	66.7M	438	1578.9	61.6
EfficientNetV2B0	29	78.7%	94.3%	7.2M	-	-	-
EfficientNetV2B1	34	79.8%	95.0%	8.2M	-	-	-
EfficientNetV2B2	42	80.5%	95.1%	10.2M	-	-	-
EfficientNetV2B3	59	82.0%	95.8%	14.5M	-	-	-
EfficientNetV2S	88	83.9%	96.7%	21.6M	-	-	-
EfficientNetV2M	220	85.3%	97.4%	54.4M	-	-	-
EfficientNetV2L	479	85.7%	97.5%	119.0M	-	-	-
ConvNeXtTiny	109.42	81.3%	-	28.6M	-	-	-
ConvNeXtSmall	192.29	82.3%	-	50.2M	-	-	-
ConvNeXtBase	338.58	85.3%	-	88.5M	-	-	-
ConvNeXtLarge	755.07	86.3%	-	197.7M	-	-	-
ConvNeXtXLarge	1310	86.7%	-	350.1M	-	-	-

MobileNetV2 model

The following is referenced from Recognize the Simpsons TensorFlow (88% acc.)

# Load the pretained model
pretrained_model = tf.keras.applications.MobileNetV2(
    input_shape=(224, 224, 3),
    include_top=False,
    weights='imagenet',
    pooling='avg'
)

pretrained_model.trainable = False

inputs = pretrained_model.input

x = tf.keras.layers.Dense(128, activation="relu")(pretrained_model.output)
x = tf.keras.layers.Dense(1024, activation="relu")(x)

outputs = tf.keras.layers.Dense(42, activation="softmax")(x)
model_MNV2 = tf.keras.Model(inputs=inputs, outputs=outputs)

model_MNV2.compile(
    optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]
)

model_MNV2.summary()

Model: "model"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               
                                )]                                                                
                                                                                                  
 Conv1 (Conv2D)                 (None, 112, 112, 32  864         ['input_1[0][0]']                
                                )                                                                 
                                                                                                  
 bn_Conv1 (BatchNormalization)  (None, 112, 112, 32  128         ['Conv1[0][0]']                  
                                )                                                                 
                                                                                                  
 Conv1_relu (ReLU)              (None, 112, 112, 32  0           ['bn_Conv1[0][0]']               
                                )                                                                 
                                                                                                  
 expanded_conv_depthwise (Depth  (None, 112, 112, 32  288        ['Conv1_relu[0][0]']             
 wiseConv2D)                    )                                                                 
                                                                                                  
 expanded_conv_depthwise_BN (Ba  (None, 112, 112, 32  128        ['expanded_conv_depthwise[0][0]']
 tchNormalization)              )                                                                 
                                                                                                  
 expanded_conv_depthwise_relu (  (None, 112, 112, 32  0          ['expanded_conv_depthwise_BN[0][0
 ReLU)                          )                                ]']                              
                                                                                                  
 expanded_conv_project (Conv2D)  (None, 112, 112, 16  512        ['expanded_conv_depthwise_relu[0]
                                )                                [0]']                            
                                                                                                  
 expanded_conv_project_BN (Batc  (None, 112, 112, 16  64         ['expanded_conv_project[0][0]']  
 hNormalization)                )                                                                 
                                                                                                  
 block_1_expand (Conv2D)        (None, 112, 112, 96  1536        ['expanded_conv_project_BN[0][0]'
                                )                                ]                                
                                                                                                  
 block_1_expand_BN (BatchNormal  (None, 112, 112, 96  384        ['block_1_expand[0][0]']         
 ization)                       )                                                                 
                                                                                                  
 block_1_expand_relu (ReLU)     (None, 112, 112, 96  0           ['block_1_expand_BN[0][0]']      
                                )                                                                 
                                                                                                  
 block_1_pad (ZeroPadding2D)    (None, 113, 113, 96  0           ['block_1_expand_relu[0][0]']    
                                )                                                                 
                                                                                                  
 block_1_depthwise (DepthwiseCo  (None, 56, 56, 96)  864         ['block_1_pad[0][0]']            
 nv2D)                                                                                            
                                                                                                  
 block_1_depthwise_BN (BatchNor  (None, 56, 56, 96)  384         ['block_1_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_1_depthwise_relu (ReLU)  (None, 56, 56, 96)   0           ['block_1_depthwise_BN[0][0]']   
                                                                                                  
 block_1_project (Conv2D)       (None, 56, 56, 24)   2304        ['block_1_depthwise_relu[0][0]'] 
                                                                                                  
 block_1_project_BN (BatchNorma  (None, 56, 56, 24)  96          ['block_1_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_2_expand (Conv2D)        (None, 56, 56, 144)  3456        ['block_1_project_BN[0][0]']     
                                                                                                  
 block_2_expand_BN (BatchNormal  (None, 56, 56, 144)  576        ['block_2_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_2_expand_relu (ReLU)     (None, 56, 56, 144)  0           ['block_2_expand_BN[0][0]']      
                                                                                                  
 block_2_depthwise (DepthwiseCo  (None, 56, 56, 144)  1296       ['block_2_expand_relu[0][0]']    
 nv2D)                                                                                            
                                                                                                  
 block_2_depthwise_BN (BatchNor  (None, 56, 56, 144)  576        ['block_2_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_2_depthwise_relu (ReLU)  (None, 56, 56, 144)  0           ['block_2_depthwise_BN[0][0]']   
                                                                                                  
 block_2_project (Conv2D)       (None, 56, 56, 24)   3456        ['block_2_depthwise_relu[0][0]'] 
                                                                                                  
 block_2_project_BN (BatchNorma  (None, 56, 56, 24)  96          ['block_2_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_2_add (Add)              (None, 56, 56, 24)   0           ['block_1_project_BN[0][0]',     
                                                                  'block_2_project_BN[0][0]']     
                                                                                                  
 block_3_expand (Conv2D)        (None, 56, 56, 144)  3456        ['block_2_add[0][0]']            
                                                                                                  
 block_3_expand_BN (BatchNormal  (None, 56, 56, 144)  576        ['block_3_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_3_expand_relu (ReLU)     (None, 56, 56, 144)  0           ['block_3_expand_BN[0][0]']      
                                                                                                  
 block_3_pad (ZeroPadding2D)    (None, 57, 57, 144)  0           ['block_3_expand_relu[0][0]']    
                                                                                                  
 block_3_depthwise (DepthwiseCo  (None, 28, 28, 144)  1296       ['block_3_pad[0][0]']            
 nv2D)                                                                                            
                                                                                                  
 block_3_depthwise_BN (BatchNor  (None, 28, 28, 144)  576        ['block_3_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_3_depthwise_relu (ReLU)  (None, 28, 28, 144)  0           ['block_3_depthwise_BN[0][0]']   
                                                                                                  
 block_3_project (Conv2D)       (None, 28, 28, 32)   4608        ['block_3_depthwise_relu[0][0]'] 
                                                                                                  
 block_3_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_3_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_4_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_3_project_BN[0][0]']     
                                                                                                  
 block_4_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_4_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_4_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_4_expand_BN[0][0]']      
                                                                                                  
 block_4_depthwise (DepthwiseCo  (None, 28, 28, 192)  1728       ['block_4_expand_relu[0][0]']    
 nv2D)                                                                                            
                                                                                                  
 block_4_depthwise_BN (BatchNor  (None, 28, 28, 192)  768        ['block_4_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_4_depthwise_relu (ReLU)  (None, 28, 28, 192)  0           ['block_4_depthwise_BN[0][0]']   
                                                                                                  
 block_4_project (Conv2D)       (None, 28, 28, 32)   6144        ['block_4_depthwise_relu[0][0]'] 
                                                                                                  
 block_4_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_4_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_4_add (Add)              (None, 28, 28, 32)   0           ['block_3_project_BN[0][0]',     
                                                                  'block_4_project_BN[0][0]']     
                                                                                                  
 block_5_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_4_add[0][0]']            
                                                                                                  
 block_5_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_5_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_5_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_5_expand_BN[0][0]']      
                                                                                                  
 block_5_depthwise (DepthwiseCo  (None, 28, 28, 192)  1728       ['block_5_expand_relu[0][0]']    
 nv2D)                                                                                            
                                                                                                  
 block_5_depthwise_BN (BatchNor  (None, 28, 28, 192)  768        ['block_5_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_5_depthwise_relu (ReLU)  (None, 28, 28, 192)  0           ['block_5_depthwise_BN[0][0]']   
                                                                                                  
 block_5_project (Conv2D)       (None, 28, 28, 32)   6144        ['block_5_depthwise_relu[0][0]'] 
                                                                                                  
 block_5_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_5_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_5_add (Add)              (None, 28, 28, 32)   0           ['block_4_add[0][0]',            
                                                                  'block_5_project_BN[0][0]']     
                                                                                                  
 block_6_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_5_add[0][0]']            
                                                                                                  
 block_6_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_6_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_6_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_6_expand_BN[0][0]']      
                                                                                                  
 block_6_pad (ZeroPadding2D)    (None, 29, 29, 192)  0           ['block_6_expand_relu[0][0]']    
                                                                                                  
 block_6_depthwise (DepthwiseCo  (None, 14, 14, 192)  1728       ['block_6_pad[0][0]']            
 nv2D)                                                                                            
                                                                                                  
 block_6_depthwise_BN (BatchNor  (None, 14, 14, 192)  768        ['block_6_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_6_depthwise_relu (ReLU)  (None, 14, 14, 192)  0           ['block_6_depthwise_BN[0][0]']   
                                                                                                  
 block_6_project (Conv2D)       (None, 14, 14, 64)   12288       ['block_6_depthwise_relu[0][0]'] 
                                                                                                  
 block_6_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_6_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_7_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_6_project_BN[0][0]']     
                                                                                                  
 block_7_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_7_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_7_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_7_expand_BN[0][0]']      
                                                                                                  
 block_7_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_7_expand_relu[0][0]']    
 nv2D)                                                                                            
                                                                                                  
 block_7_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_7_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_7_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_7_depthwise_BN[0][0]']   
                                                                                                  
 block_7_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_7_depthwise_relu[0][0]'] 
                                                                                                  
 block_7_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_7_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_7_add (Add)              (None, 14, 14, 64)   0           ['block_6_project_BN[0][0]',     
                                                                  'block_7_project_BN[0][0]']     
                                                                                                  
 block_8_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_7_add[0][0]']            
                                                                                                  
 block_8_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_8_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_8_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_8_expand_BN[0][0]']      
                                                                                                  
 block_8_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_8_expand_relu[0][0]']    
 nv2D)                                                                                            
                                                                                                  
 block_8_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_8_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_8_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_8_depthwise_BN[0][0]']   
                                                                                                  
 block_8_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_8_depthwise_relu[0][0]'] 
                                                                                                  
 block_8_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_8_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_8_add (Add)              (None, 14, 14, 64)   0           ['block_7_add[0][0]',            
                                                                  'block_8_project_BN[0][0]']     
                                                                                                  
 block_9_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_8_add[0][0]']            
                                                                                                  
 block_9_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_9_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_9_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_9_expand_BN[0][0]']      
                                                                                                  
 block_9_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_9_expand_relu[0][0]']    
 nv2D)                                                                                            
                                                                                                  
 block_9_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_9_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_9_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_9_depthwise_BN[0][0]']   
                                                                                                  
 block_9_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_9_depthwise_relu[0][0]'] 
                                                                                                  
 block_9_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_9_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_9_add (Add)              (None, 14, 14, 64)   0           ['block_8_add[0][0]',            
                                                                  'block_9_project_BN[0][0]']     
                                                                                                  
 block_10_expand (Conv2D)       (None, 14, 14, 384)  24576       ['block_9_add[0][0]']            
                                                                                                  
 block_10_expand_BN (BatchNorma  (None, 14, 14, 384)  1536       ['block_10_expand[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_10_expand_relu (ReLU)    (None, 14, 14, 384)  0           ['block_10_expand_BN[0][0]']     
                                                                                                  
 block_10_depthwise (DepthwiseC  (None, 14, 14, 384)  3456       ['block_10_expand_relu[0][0]']   
 onv2D)                                                                                           
                                                                                                  
 block_10_depthwise_BN (BatchNo  (None, 14, 14, 384)  1536       ['block_10_depthwise[0][0]']     
 rmalization)                                                                                     
                                                                                                  
 block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0          ['block_10_depthwise_BN[0][0]']  
                                                                                                  
 block_10_project (Conv2D)      (None, 14, 14, 96)   36864       ['block_10_depthwise_relu[0][0]']
                                                                                                  
 block_10_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_10_project[0][0]']       
 alization)                                                                                       
                                                                                                  
 block_11_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_10_project_BN[0][0]']    
                                                                                                  
 block_11_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_11_expand[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_11_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_11_expand_BN[0][0]']     
                                                                                                  
 block_11_depthwise (DepthwiseC  (None, 14, 14, 576)  5184       ['block_11_expand_relu[0][0]']   
 onv2D)                                                                                           
                                                                                                  
 block_11_depthwise_BN (BatchNo  (None, 14, 14, 576)  2304       ['block_11_depthwise[0][0]']     
 rmalization)                                                                                     
                                                                                                  
 block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0          ['block_11_depthwise_BN[0][0]']  
                                                                                                  
 block_11_project (Conv2D)      (None, 14, 14, 96)   55296       ['block_11_depthwise_relu[0][0]']
                                                                                                  
 block_11_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_11_project[0][0]']       
 alization)                                                                                       
                                                                                                  
 block_11_add (Add)             (None, 14, 14, 96)   0           ['block_10_project_BN[0][0]',    
                                                                  'block_11_project_BN[0][0]']    
                                                                                                  
 block_12_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_11_add[0][0]']           
                                                                                                  
 block_12_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_12_expand[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_12_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_12_expand_BN[0][0]']     
                                                                                                  
 block_12_depthwise (DepthwiseC  (None, 14, 14, 576)  5184       ['block_12_expand_relu[0][0]']   
 onv2D)                                                                                           
                                                                                                  
 block_12_depthwise_BN (BatchNo  (None, 14, 14, 576)  2304       ['block_12_depthwise[0][0]']     
 rmalization)                                                                                     
                                                                                                  
 block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0          ['block_12_depthwise_BN[0][0]']  
                                                                                                  
 block_12_project (Conv2D)      (None, 14, 14, 96)   55296       ['block_12_depthwise_relu[0][0]']
                                                                                                  
 block_12_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_12_project[0][0]']       
 alization)                                                                                       
                                                                                                  
 block_12_add (Add)             (None, 14, 14, 96)   0           ['block_11_add[0][0]',           
                                                                  'block_12_project_BN[0][0]']    
                                                                                                  
 block_13_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_12_add[0][0]']           
                                                                                                  
 block_13_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_13_expand[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_13_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_13_expand_BN[0][0]']     
                                                                                                  
 block_13_pad (ZeroPadding2D)   (None, 15, 15, 576)  0           ['block_13_expand_relu[0][0]']   
                                                                                                  
 block_13_depthwise (DepthwiseC  (None, 7, 7, 576)   5184        ['block_13_pad[0][0]']           
 onv2D)                                                                                           
                                                                                                  
 block_13_depthwise_BN (BatchNo  (None, 7, 7, 576)   2304        ['block_13_depthwise[0][0]']     
 rmalization)                                                                                     
                                                                                                  
 block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)   0           ['block_13_depthwise_BN[0][0]']  
                                                                                                  
 block_13_project (Conv2D)      (None, 7, 7, 160)    92160       ['block_13_depthwise_relu[0][0]']
                                                                                                  
 block_13_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_13_project[0][0]']       
 alization)                                                                                       
                                                                                                  
 block_14_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_13_project_BN[0][0]']    
                                                                                                  
 block_14_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_14_expand[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_14_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_14_expand_BN[0][0]']     
                                                                                                  
 block_14_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_14_expand_relu[0][0]']   
 onv2D)                                                                                           
                                                                                                  
 block_14_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_14_depthwise[0][0]']     
 rmalization)                                                                                     
                                                                                                  
 block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_14_depthwise_BN[0][0]']  
                                                                                                  
 block_14_project (Conv2D)      (None, 7, 7, 160)    153600      ['block_14_depthwise_relu[0][0]']
                                                                                                  
 block_14_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_14_project[0][0]']       
 alization)                                                                                       
                                                                                                  
 block_14_add (Add)             (None, 7, 7, 160)    0           ['block_13_project_BN[0][0]',    
                                                                  'block_14_project_BN[0][0]']    
                                                                                                  
 block_15_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_14_add[0][0]']           
                                                                                                  
 block_15_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_15_expand[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_15_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_15_expand_BN[0][0]']     
                                                                                                  
 block_15_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_15_expand_relu[0][0]']   
 onv2D)                                                                                           
                                                                                                  
 block_15_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_15_depthwise[0][0]']     
 rmalization)                                                                                     
                                                                                                  
 block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_15_depthwise_BN[0][0]']  
                                                                                                  
 block_15_project (Conv2D)      (None, 7, 7, 160)    153600      ['block_15_depthwise_relu[0][0]']
                                                                                                  
 block_15_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_15_project[0][0]']       
 alization)                                                                                       
                                                                                                  
 block_15_add (Add)             (None, 7, 7, 160)    0           ['block_14_add[0][0]',           
                                                                  'block_15_project_BN[0][0]']    
                                                                                                  
 block_16_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_15_add[0][0]']           
                                                                                                  
 block_16_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_16_expand[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_16_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_16_expand_BN[0][0]']     
                                                                                                  
 block_16_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_16_expand_relu[0][0]']   
 onv2D)                                                                                           
                                                                                                  
 block_16_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_16_depthwise[0][0]']     
 rmalization)                                                                                     
                                                                                                  
 block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_16_depthwise_BN[0][0]']  
                                                                                                  
 block_16_project (Conv2D)      (None, 7, 7, 320)    307200      ['block_16_depthwise_relu[0][0]']
                                                                                                  
 block_16_project_BN (BatchNorm  (None, 7, 7, 320)   1280        ['block_16_project[0][0]']       
 alization)                                                                                       
                                                                                                  
 Conv_1 (Conv2D)                (None, 7, 7, 1280)   409600      ['block_16_project_BN[0][0]']    
                                                                                                  
 Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)  5120        ['Conv_1[0][0]']                 
                                                                                                  
 out_relu (ReLU)                (None, 7, 7, 1280)   0           ['Conv_1_bn[0][0]']              
                                                                                                  
 global_average_pooling2d (Glob  (None, 1280)        0           ['out_relu[0][0]']               
 alAveragePooling2D)                                                                              
                                                                                                  
 dense_2 (Dense)                (None, 128)          163968      ['global_average_pooling2d[0][0]'
                                                                 ]                                
                                                                                                  
 dense_3 (Dense)                (None, 1024)         132096      ['dense_2[0][0]']                
                                                                                                  
 dense_4 (Dense)                (None, 42)           43050       ['dense_3[0][0]']                
                                                                                                  
==================================================================================================
Total params: 2,597,098
Trainable params: 339,114
Non-trainable params: 2,257,984
__________________________________________________________________________________________________

model_MNV2.compile(
    optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]
)

checkpoint_filepath_MNV2 = "./models/MNV2.h5"

model_MNV2_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
    filepath=checkpoint_filepath_MNV2,
    save_weights_only=False,
    monitor="val_accuracy",
    mode="max",
    save_best_only=True,
)

callbacks_MNV2 = [
    model_MNV2_checkpoint_callback,
    EarlyStopping(monitor="val_accuracy", patience=5, restore_best_weights=True),
]

history_MNV2 = model_MNV2.fit(
    training_set, validation_data=val_set, epochs=50, callbacks=callbacks_MNV2
)

Epoch 1/50
244/244 [==============================] - 108s 434ms/step - loss: 2.4468 - accuracy: 0.3495 - val_loss: 1.8542 - val_accuracy: 0.4954
Epoch 2/50
244/244 [==============================] - 106s 435ms/step - loss: 1.7848 - accuracy: 0.5089 - val_loss: 1.7979 - val_accuracy: 0.5104
Epoch 3/50
244/244 [==============================] - 106s 434ms/step - loss: 1.5545 - accuracy: 0.5696 - val_loss: 1.5409 - val_accuracy: 0.5712
Epoch 4/50
244/244 [==============================] - 106s 433ms/step - loss: 1.4255 - accuracy: 0.5974 - val_loss: 1.5524 - val_accuracy: 0.5678
Epoch 5/50
244/244 [==============================] - 105s 429ms/step - loss: 1.3348 - accuracy: 0.6188 - val_loss: 1.4764 - val_accuracy: 0.5861
Epoch 6/50
244/244 [==============================] - 106s 433ms/step - loss: 1.2664 - accuracy: 0.6415 - val_loss: 1.4505 - val_accuracy: 0.5953
Epoch 7/50
244/244 [==============================] - 106s 433ms/step - loss: 1.2224 - accuracy: 0.6489 - val_loss: 1.4238 - val_accuracy: 0.5996
Epoch 8/50
244/244 [==============================] - 105s 432ms/step - loss: 1.1595 - accuracy: 0.6668 - val_loss: 1.4417 - val_accuracy: 0.6088
Epoch 9/50
244/244 [==============================] - 107s 437ms/step - loss: 1.1099 - accuracy: 0.6773 - val_loss: 1.3215 - val_accuracy: 0.6213
Epoch 10/50
244/244 [==============================] - 106s 433ms/step - loss: 1.0765 - accuracy: 0.6885 - val_loss: 1.3723 - val_accuracy: 0.6228
Epoch 11/50
244/244 [==============================] - 106s 433ms/step - loss: 1.0483 - accuracy: 0.6939 - val_loss: 1.3970 - val_accuracy: 0.6107
Epoch 12/50
244/244 [==============================] - 106s 433ms/step - loss: 1.0338 - accuracy: 0.6988 - val_loss: 1.3952 - val_accuracy: 0.6141
Epoch 13/50
244/244 [==============================] - 106s 435ms/step - loss: 1.0124 - accuracy: 0.7035 - val_loss: 1.3422 - val_accuracy: 0.6343
Epoch 14/50
244/244 [==============================] - 105s 432ms/step - loss: 0.9675 - accuracy: 0.7169 - val_loss: 1.3468 - val_accuracy: 0.6295
Epoch 15/50
244/244 [==============================] - 106s 435ms/step - loss: 0.9460 - accuracy: 0.7221 - val_loss: 1.2937 - val_accuracy: 0.6334
Epoch 16/50
244/244 [==============================] - 106s 436ms/step - loss: 0.9322 - accuracy: 0.7274 - val_loss: 1.3918 - val_accuracy: 0.6165
Epoch 17/50
244/244 [==============================] - 105s 432ms/step - loss: 0.9245 - accuracy: 0.7264 - val_loss: 1.5238 - val_accuracy: 0.5871
Epoch 18/50
244/244 [==============================] - 106s 435ms/step - loss: 0.9065 - accuracy: 0.7315 - val_loss: 1.2946 - val_accuracy: 0.6503
Epoch 19/50
244/244 [==============================] - 105s 432ms/step - loss: 0.9094 - accuracy: 0.7346 - val_loss: 1.3655 - val_accuracy: 0.6397
Epoch 20/50
244/244 [==============================] - 105s 430ms/step - loss: 0.8810 - accuracy: 0.7383 - val_loss: 1.3503 - val_accuracy: 0.6310
Epoch 21/50
244/244 [==============================] - 106s 433ms/step - loss: 0.8728 - accuracy: 0.7423 - val_loss: 1.3489 - val_accuracy: 0.6329
Epoch 22/50
244/244 [==============================] - 105s 431ms/step - loss: 0.8550 - accuracy: 0.7450 - val_loss: 1.3282 - val_accuracy: 0.6387
Epoch 23/50
244/244 [==============================] - 106s 434ms/step - loss: 0.8492 - accuracy: 0.7506 - val_loss: 1.3459 - val_accuracy: 0.6358

Load highest val-accuracy model - Mobile Net V2

best_from_custome_MNV2_model = keras.models.load_model(checkpoint_filepath_MNV2)

ResNet50

The following is referenced from CNN + ResNet

from tensorflow.keras.applications.resnet50 import ResNet50
import tensorflow.keras.optimizers.legacy

model_RN50=Sequential()
model_RN50.add(ResNet50(include_top=False,
               pooling='max'))
model_RN50.add(Flatten())
model_RN50.add(Dense(1024, activation="relu"))
model_RN50.add(Dropout(0.5))
model_RN50.add(Dense(len(labels), activation='softmax'))

model_RN50.summary()

Model: "sequential_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 resnet50 (Functional)       (None, 2048)              23587712  
                                                                 
 flatten_1 (Flatten)         (None, 2048)              0         
                                                                 
 dense_5 (Dense)             (None, 1024)              2098176   
                                                                 
 dropout_4 (Dropout)         (None, 1024)              0         
                                                                 
 dense_6 (Dense)             (None, 42)                43050     
                                                                 
=================================================================
Total params: 25,728,938
Trainable params: 25,675,818
Non-trainable params: 53,120
_________________________________________________________________

model_RN50.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

checkpoint_filepath_RN50 = "./models/RN50.h5"

model_RN50_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
    filepath=checkpoint_filepath_RN50,
    save_weights_only=False,
    monitor="val_accuracy",
    mode="max",
    save_best_only=True,
)

callbacks_MNV2 = [
    model_RN50_checkpoint_callback,
    EarlyStopping(monitor="val_accuracy", patience=5, restore_best_weights=True),
]

training_set_RN50 = train_datagen.flow_from_directory(
    train_dir,
    target_size=image_size[:2],
    batch_size=35,
    class_mode="categorical",
    color_mode="rgb",
)

val_set_RN50 = test_datagen.flow_from_directory(
    val_dir,
    target_size=image_size[:2],
    batch_size=35,
    class_mode="categorical",
    color_mode="rgb",
)

Found 24395 images belonging to 42 classes.
Found 2073 images belonging to 42 classes.

history_RN50 = model_RN50.fit(
    training_set_RN50, epochs=50, validation_data=val_set_RN50, callbacks=callbacks_MNV2
)

Epoch 1/50
697/697 [==============================] - 125s 173ms/step - loss: 3.2562 - accuracy: 0.2537 - val_loss: 4.0804 - val_accuracy: 0.0145
Epoch 2/50
697/697 [==============================] - 120s 172ms/step - loss: 1.6030 - accuracy: 0.5683 - val_loss: 1.8252 - val_accuracy: 0.5297
Epoch 3/50
697/697 [==============================] - 120s 173ms/step - loss: 1.3147 - accuracy: 0.6454 - val_loss: 0.8303 - val_accuracy: 0.7771
Epoch 4/50
697/697 [==============================] - 120s 172ms/step - loss: 1.1980 - accuracy: 0.6780 - val_loss: 5.2982 - val_accuracy: 0.0550
Epoch 5/50
697/697 [==============================] - 120s 172ms/step - loss: 1.0553 - accuracy: 0.7139 - val_loss: 2.3258 - val_accuracy: 0.4568
Epoch 6/50
697/697 [==============================] - 120s 172ms/step - loss: 0.8492 - accuracy: 0.7779 - val_loss: 1.0132 - val_accuracy: 0.7192
Epoch 7/50
697/697 [==============================] - 120s 172ms/step - loss: 1.1842 - accuracy: 0.6836 - val_loss: 1.1694 - val_accuracy: 0.6898
Epoch 8/50
697/697 [==============================] - 121s 173ms/step - loss: 0.8175 - accuracy: 0.7804 - val_loss: 0.6165 - val_accuracy: 0.8249
Epoch 9/50
697/697 [==============================] - 120s 173ms/step - loss: 0.7473 - accuracy: 0.7996 - val_loss: 3.4145 - val_accuracy: 0.1630
Epoch 10/50
697/697 [==============================] - 121s 174ms/step - loss: 0.7452 - accuracy: 0.8001 - val_loss: 0.6542 - val_accuracy: 0.8215
Epoch 11/50
697/697 [==============================] - 121s 173ms/step - loss: 0.7178 - accuracy: 0.8089 - val_loss: 1.9048 - val_accuracy: 0.5355
Epoch 12/50
697/697 [==============================] - 121s 174ms/step - loss: 0.7527 - accuracy: 0.7970 - val_loss: 0.3887 - val_accuracy: 0.8905
Epoch 13/50
697/697 [==============================] - 120s 172ms/step - loss: 0.9015 - accuracy: 0.7684 - val_loss: 4.5562 - val_accuracy: 0.2595
Epoch 14/50
697/697 [==============================] - 120s 173ms/step - loss: 0.7467 - accuracy: 0.7973 - val_loss: 0.4030 - val_accuracy: 0.8866
Epoch 15/50
697/697 [==============================] - 121s 173ms/step - loss: 0.6788 - accuracy: 0.8173 - val_loss: 1.0538 - val_accuracy: 0.7289
Epoch 16/50
697/697 [==============================] - 121s 173ms/step - loss: 0.7552 - accuracy: 0.7937 - val_loss: 0.7005 - val_accuracy: 0.8027
Epoch 17/50
697/697 [==============================] - 121s 173ms/step - loss: 0.6447 - accuracy: 0.8232 - val_loss: 0.8102 - val_accuracy: 0.7815

Load highest val-accuracy model - Res Net 50

best_from_custome_RN50_model = keras.models.load_model(checkpoint_filepath_RN50)

Evaluation

acc_customized = history_customized.history["accuracy"]
val_acc_customized = history_customized.history["val_accuracy"]
loss_customized = history_customized.history["loss"]
val_loss_customized = history_customized.history["val_loss"]
epochs_customized = range(1, len(acc_customized) + 1)

acc_MNV2 = history_MNV2.history["accuracy"]
val_acc_MNV2 = history_MNV2.history["val_accuracy"]
loss_MNV2 = history_MNV2.history["loss"]
val_loss_MNV2 = history_MNV2.history["val_loss"]
epochs_MNV2 = range(1, len(acc_MNV2) + 1)

acc_RN50 = history_RN50.history["accuracy"]
val_acc_RN50 = history_RN50.history["val_accuracy"]
loss_RN50 = history_RN50.history["loss"]
val_loss_RN50 = history_RN50.history["val_loss"]
epochs_RN50 = range(1, len(acc_RN50) + 1)

Accuracy Comparison between these thee models

fig, axs = plt.subplots(1, 3, figsize=(24, 8))

axs[0].plot(epochs_customized, acc_customized, "bo", label="Training accuracy")
axs[0].plot(epochs_customized, val_acc_customized, "b", label="Validation accuracy")
axs[0].set_title("Training and validation accuracy trained by customized model")
axs[0].legend()


axs[1].plot(epochs_MNV2, acc_MNV2, "bo", label="Training accuracy")
axs[1].plot(epochs_MNV2, val_acc_MNV2, "b", label="Validation accuracy")
axs[1].set_title("Training and validation accuracy trained by Mobile Net V2")
axs[1].legend()


axs[2].plot(epochs_RN50, acc_RN50, "bo", label="Training accuracy")
axs[2].plot(epochs_RN50, val_acc_RN50, "b", label="Validation accuracy")
axs[2].set_title("Training and validation accuracy trained by ResNet50")
axs[2].legend()

<matplotlib.legend.Legend at 0x1c31bb43850>

Loss Comparison between these thee models

fig, axs = plt.subplots(1, 3, figsize=(24, 8))

axs[0].plot(epochs_customized, loss_customized, "bo", label="Training loss")
axs[0].plot(epochs_customized, val_loss_customized, "b", label="Validation loss")
axs[0].set_title("Training and validation loss trained by customized model")
axs[0].legend()

axs[1].plot(epochs_MNV2, loss_MNV2, "bo", label="Training loss")
axs[1].plot(epochs_MNV2, val_loss_MNV2, "b", label="Validation loss")
axs[1].set_title("Training and validation loss trained by Mobile Net V2")
axs[1].legend()

axs[2].plot(epochs_RN50, loss_RN50, "bo", label="Training loss")
axs[2].plot(epochs_RN50, val_loss_RN50, "b", label="Validation loss")
axs[2].set_title("Training and validation loss trained by ResNet50")
axs[2].legend()

<matplotlib.legend.Legend at 0x1c3324d7490>

Predictions

best_from_custome_customized_model
best_from_custome_MNV2_model
best_from_custome_RN50_model

<keras.engine.sequential.Sequential at 0x1c11014e370>

y_true = test_set.classes
y_test_true = keras.utils.to_categorical(y_true, num_classes=len(labels))
y_test_true = np.argmax(y_test_true, axis=1)

Evaluation of customized model on test set

y_pred_customized = best_from_custome_customized_model.predict(test_set)
y_pred_customized = np.argmax(y_pred_customized, axis=1)

22/22 [==============================] - 12s 552ms/step

def find_unequal_indexes(list1, list2):
    unequal_indices = []
    min_length = min(len(list1), len(list2))
    
    for i in range(min_length):
        if list1[i] != list2[i]:
            unequal_indices.append(i+1)
            
    for i in range(min_length, max(len(list1), len(list2))):
        unequal_indices.append(i+1)
    
    return unequal_indices

Total number of mis-classification by customized model

unequal_indices_customized = find_unequal_indexes(y_test_true, y_pred_customized)
print("Total number of mis-classification by customized model:", len(unequal_indices_customized))

Total number of mis-classification by customized model: 168

Classification Report

class_report_customized = classification_report(
    y_test_true, y_pred_customized, zero_division=1, target_names=labels
)
print(class_report_customized)

                          precision    recall  f1-score   support

  abraham_grampa_simpson       0.94      0.88      0.91        92
           agnes_skinner       1.00      0.60      0.75         5
  apu_nahasapeemapetilon       0.90      0.90      0.90        63
           barney_gumble       0.90      0.75      0.82        12
            bart_simpson       0.88      0.91      0.89       135
            carl_carlson       0.58      1.00      0.73        11
charles_montgomery_burns       0.94      0.95      0.95       120
            chief_wiggum       0.93      0.94      0.94       100
         cletus_spuckler       0.80      0.67      0.73         6
          comic_book_guy       0.84      0.88      0.86        48
               disco_stu       1.00      0.50      0.67         2
          edna_krabappel       0.95      0.79      0.86        47
                fat_tony       1.00      1.00      1.00         4
                     gil       0.75      0.75      0.75         4
    groundskeeper_willie       0.76      1.00      0.87        13
           homer_simpson       0.92      0.96      0.94       226
           kent_brockman       0.96      0.92      0.94        51
        krusty_the_clown       0.92      0.98      0.95       122
           lenny_leonard       0.97      0.90      0.93        31
             lionel_hutz       1.00      1.00      1.00         1
            lisa_simpson       0.97      0.86      0.91       136
          maggie_simpson       1.00      0.57      0.73        14
           marge_simpson       0.98      0.96      0.97       130
           martin_prince       0.78      0.88      0.82         8
            mayor_quimby       0.85      0.88      0.87        26
     milhouse_van_houten       0.98      0.96      0.97       109
             miss_hoover       0.75      1.00      0.86         3
             moe_szyslak       0.95      0.93      0.94       146
            ned_flanders       0.94      0.96      0.95       146
            nelson_muntz       0.97      0.84      0.90        37
               otto_mann       0.57      1.00      0.73         4
           patty_bouvier       0.88      0.88      0.88         8
       principal_skinner       0.95      0.94      0.95       120
    professor_john_frink       0.35      0.86      0.50         7
      rainier_wolfcastle       0.62      1.00      0.77         5
            ralph_wiggum       1.00      0.90      0.95        10
           selma_bouvier       0.71      0.91      0.80        11
            sideshow_bob       0.94      0.96      0.95        89
            sideshow_mel       0.60      0.75      0.67         4
          snake_jailbird       0.83      0.83      0.83         6
            troy_mcclure       1.00      1.00      1.00         2
         waylon_smithers       0.87      0.68      0.76        19

                accuracy                           0.92      2133
               macro avg       0.87      0.88      0.86      2133
            weighted avg       0.93      0.92      0.92      2133

Support amount versus Precision

def plot_support_train_amt(report):
    precision_list = []
    support_list = []
    seq = 0
    for val in report.values():
        if seq != 42:
            precision_list.append(val['precision'])
            support_list.append(val['support'])
            seq+=1

    fig, axs = plt.subplots(1, 2, figsize=(12, 6))
            
    axs[0].scatter(precision_list, support_list)
    axs[0].set_xlabel('Precision')
    axs[0].set_ylabel('Support amount') 

    axs[1].scatter(precision_list, len_list_)
    axs[1].set_xlabel('Precision')
    axs[1].set_ylabel('Training data set amount') 

class_report_customized = classification_report(
    y_test_true, y_pred_customized, zero_division=1, output_dict=True
)

plot_support_train_amt(class_report_customized)

from sklearn.metrics import confusion_matrix
import seaborn as sns

def plot_confusion_matrix(cf):
    plt.figure(figsize=(20, 15))
    sns.heatmap(
        cf,
        annot=False,
        xticklabels=labels,
        yticklabels=labels,
    )
    plt.title("Normalized Confusion Matrix", fontsize=23)
    plt.show()

cf_matrix_customized = confusion_matrix(y_test_true, y_pred_customized, normalize="true")
plot_confusion_matrix(cf_matrix_customized)

def plot_specific_images(a, b):
    plt.figure(figsize=(15, 18))
    for i, class_1 in enumerate([a, b]):

        folder_path = f"{train_dir}/{class_1}"
        files = os.listdir(folder_path)
        files = [f for f in files if os.path.isfile(os.path.join(folder_path, f))]
        random_file = random.choice(files)

        plt.subplot(2, 2, i + 1)
        img = mpimg.imread(f"{folder_path}/{random_file}")
        plt.imshow(img)
        plt.axis("off")
        plt.title(class_1)
    plt.tight_layout()
    plt.show()

plot_specific_images('disco_stu', 'comic_book_guy')

Predict on 15 images to see the results

import cv2

def predict_15_imgs(m):
    # Display 15 picture of the dataset with their labels
    fig, axes = plt.subplots(
        nrows=4, ncols=5, figsize=(15, 12), subplot_kw={"xticks": [], "yticks": []}
    )

    for i, ax in enumerate(axes.flat):
        dd = random.randint(1, 41)
        class_name = labels[dd]

        folder_path = f"{train_dir}/{class_name}"
        files = os.listdir(folder_path)
        files = [f for f in files if os.path.isfile(os.path.join(folder_path, f))]
        random_file = random.choice(files)

        file_name = folder_path + "/" + random_file

        img = cv2.imread(file_name)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

        img = cv2.resize(img, (180, 180))
        img = img.astype("float32")

        img_tensor = tf.expand_dims(tf.convert_to_tensor(img), axis=0)
        pred = m.predict(img_tensor)
        
        true_num = np.argmax(pred, axis=1)
        ax.imshow(plt.imread(file_name))
        ax.set_title(f"True: {class_name}\n Predicted: {labels[true_num[0]]}")
    plt.tight_layout()
    plt.show()

predict_15_imgs(best_from_custome_customized_model)

1/1 [==============================] - 0s 17ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 15ms/step
1/1 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 15ms/step
1/1 [==============================] - 0s 15ms/step
1/1 [==============================] - 0s 15ms/step
1/1 [==============================] - 0s 15ms/step
1/1 [==============================] - 0s 15ms/step
1/1 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 15ms/step
1/1 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 15ms/step
1/1 [==============================] - 0s 15ms/step
1/1 [==============================] - 0s 15ms/step

Evaluation of ResNet50 model on test set

y_pred_RN50 = best_from_custome_RN50_model.predict(test_set)
y_pred_RN50 = np.argmax(y_pred_RN50, axis=1)

22/22 [==============================] - 4s 136ms/step

Total number of mis-classifications

unequal_indices_RN50 = find_unequal_indexes(y_test_true, y_pred_RN50)

print("Total number of mis-classification by ResNet50:", len(unequal_indices_RN50))

Total number of mis-classification by ResNet50: 254

Classification Report

class_report_RN50 = classification_report(
    y_test_true, y_pred_RN50, zero_division=1, target_names=labels
)
print(class_report_RN50)

                          precision    recall  f1-score   support

  abraham_grampa_simpson       0.96      0.93      0.95        92
           agnes_skinner       0.50      0.60      0.55         5
  apu_nahasapeemapetilon       0.94      0.81      0.87        63
           barney_gumble       0.70      0.58      0.64        12
            bart_simpson       0.96      0.84      0.90       135
            carl_carlson       0.37      0.91      0.53        11
charles_montgomery_burns       0.95      0.86      0.90       120
            chief_wiggum       0.92      0.91      0.91       100
         cletus_spuckler       0.83      0.83      0.83         6
          comic_book_guy       0.77      0.83      0.80        48
               disco_stu       1.00      0.50      0.67         2
          edna_krabappel       0.91      0.83      0.87        47
                fat_tony       0.33      1.00      0.50         4
                     gil       0.67      0.50      0.57         4
    groundskeeper_willie       0.52      1.00      0.68        13
           homer_simpson       0.86      0.93      0.89       226
           kent_brockman       0.84      0.96      0.90        51
        krusty_the_clown       0.91      0.96      0.94       122
           lenny_leonard       1.00      0.71      0.83        31
             lionel_hutz       0.50      1.00      0.67         1
            lisa_simpson       0.92      0.88      0.90       136
          maggie_simpson       0.89      0.57      0.70        14
           marge_simpson       0.90      0.93      0.91       130
           martin_prince       0.58      0.88      0.70         8
            mayor_quimby       0.68      0.58      0.62        26
     milhouse_van_houten       0.97      0.93      0.95       109
             miss_hoover       0.67      0.67      0.67         3
             moe_szyslak       0.95      0.86      0.90       146
            ned_flanders       0.97      0.96      0.96       146
            nelson_muntz       1.00      0.70      0.83        37
               otto_mann       0.67      1.00      0.80         4
           patty_bouvier       0.78      0.88      0.82         8
       principal_skinner       0.92      0.90      0.91       120
    professor_john_frink       0.56      0.71      0.63         7
      rainier_wolfcastle       0.38      1.00      0.56         5
            ralph_wiggum       0.90      0.90      0.90        10
           selma_bouvier       0.42      0.91      0.57        11
            sideshow_bob       0.96      0.88      0.92        89
            sideshow_mel       1.00      1.00      1.00         4
          snake_jailbird       0.29      1.00      0.44         6
            troy_mcclure       1.00      1.00      1.00         2
         waylon_smithers       1.00      0.42      0.59        19

                accuracy                           0.88      2133
               macro avg       0.78      0.83      0.78      2133
            weighted avg       0.90      0.88      0.89      2133

Support amount versus Precision

class_report_RN50 = classification_report(
    y_test_true, y_pred_RN50, zero_division=1, output_dict=True
)

plot_support_train_amt(class_report_RN50)

cf_matrix_RN50 = confusion_matrix(y_test_true, y_pred_RN50, normalize="true")
plot_confusion_matrix(cf_matrix_RN50)

Plot the images that are most likely to be misclassified by the model

plot_specific_images('miss_hoover', 'comic_book_guy')

Evaluation of Mobile Net V2 on test set

y_pred_MNV2 = best_from_custome_MNV2_model.predict(test_set)
y_pred_MNV2 = np.argmax(y_pred_MNV2, axis=1)

22/22 [==============================] - 3s 104ms/step

Total number of mis-classifications

unequal_indices_MNV2 = find_unequal_indexes(y_test_true, y_pred_MNV2)

print("Total number of mis-classification by ResNet50:", len(unequal_indices_MNV2))

Total number of mis-classification by ResNet50: 722

class_report_MNV2 = classification_report(
    y_test_true, y_pred_MNV2, zero_division=1, target_names=labels
)
print(class_report_MNV2)

                          precision    recall  f1-score   support

  abraham_grampa_simpson       0.70      0.70      0.70        92
           agnes_skinner       0.33      0.40      0.36         5
  apu_nahasapeemapetilon       0.86      0.78      0.82        63
           barney_gumble       0.33      0.50      0.40        12
            bart_simpson       0.68      0.81      0.74       135
            carl_carlson       0.42      0.91      0.57        11
charles_montgomery_burns       0.78      0.67      0.72       120
            chief_wiggum       0.85      0.72      0.78       100
         cletus_spuckler       0.09      0.50      0.15         6
          comic_book_guy       0.78      0.81      0.80        48
               disco_stu       1.00      0.50      0.67         2
          edna_krabappel       0.96      0.53      0.68        47
                fat_tony       0.50      0.50      0.50         4
                     gil       0.20      0.25      0.22         4
    groundskeeper_willie       0.33      0.62      0.43        13
           homer_simpson       0.57      0.78      0.66       226
           kent_brockman       0.75      0.80      0.77        51
        krusty_the_clown       0.92      0.65      0.76       122
           lenny_leonard       0.56      0.32      0.41        31
             lionel_hutz       1.00      0.00      0.00         1
            lisa_simpson       0.81      0.65      0.72       136
          maggie_simpson       0.17      0.07      0.10        14
           marge_simpson       0.83      0.73      0.78       130
           martin_prince       0.10      0.12      0.11         8
            mayor_quimby       0.75      0.12      0.20        26
     milhouse_van_houten       0.88      0.56      0.69       109
             miss_hoover       1.00      0.33      0.50         3
             moe_szyslak       0.60      0.68      0.64       146
            ned_flanders       0.55      0.67      0.60       146
            nelson_muntz       0.30      0.46      0.37        37
               otto_mann       1.00      0.50      0.67         4
           patty_bouvier       0.33      0.62      0.43         8
       principal_skinner       0.85      0.55      0.67       120
    professor_john_frink       0.44      0.57      0.50         7
      rainier_wolfcastle       0.67      0.40      0.50         5
            ralph_wiggum       0.33      0.70      0.45        10
           selma_bouvier       0.22      0.36      0.28        11
            sideshow_bob       0.80      0.79      0.80        89
            sideshow_mel       0.12      0.50      0.20         4
          snake_jailbird       0.33      0.33      0.33         6
            troy_mcclure       0.67      1.00      0.80         2
         waylon_smithers       1.00      0.11      0.19        19

                accuracy                           0.66      2133
               macro avg       0.60      0.54      0.52      2133
            weighted avg       0.71      0.66      0.67      2133

Support amount versus Precision

class_report_MNV2 = classification_report(
    y_test_true, y_pred_MNV2, zero_division=1, output_dict=True
)

plot_support_train_amt(class_report_MNV2)

cf_matrix_MNV2 = confusion_matrix(y_test_true, y_pred_MNV2, normalize="true")
plot_confusion_matrix(cf_matrix_MNV2)

plot_specific_images('lionel_hutz', 'principal_skinner')

Model Comparison

import pandas as pd
comparision = {
    "Training Parameters": ['105,705,034', '339,114', '25,728,938'],
    "Model training time": ['91m 53s', '40m 34s', '34m 14s'],
    "Lowest loss": ['0.2658', '1.2937', '0.3887'],
    "with accuracy": ['93%', '63%', '89%'],
    "Highest accuracy": ['93%', '64%', '89%'],
    "with loss": ['0.2981', '1.3655', '0.3887'],
}
com_df = pd.DataFrame(comparision)
com_df

	Training Parameters	Model training time	Lowest loss	with accuracy	Highest accuracy	with loss
0	105,705,034	91m 53s	0.2658	93%	93%	0.2981
1	339,114	40m 34s	1.2937	63%	64%	1.3655
2	25,728,938	34m 14s	0.3887	89%	89%	0.3887

import matplotlib.pyplot as plt
import numpy as np

# 資料
comparison = {
    "Training Parameters": ['105,705,034', '339,114', '25,728,938'],
    "Model training time": ['91', '40', '34'],
    "Lowest loss": ['0.2658', '1.2937', '0.3887'],
    "with accuracy": ['93%', '63%', '89%'],
}

# 資料解析
parameters = [int(param.replace(',', '')) for param in comparison["Training Parameters"]]
training_time = [int(time.split(' ')[0]) for time in comparison["Model training time"]]
lowest_loss = [float(loss) for loss in comparison["Lowest loss"]]
accuracy = [int(acc.replace('%', '')) for acc in comparison["with accuracy"]]

fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(111, projection='3d')

ax.scatter(parameters, training_time, lowest_loss, c=accuracy, cmap='viridis', s=100)

ax.set_xlabel('Training Parameters')
ax.set_ylabel('Model training time (minutes)')
ax.set_zlabel('Loss')

cbar = plt.colorbar(ax.scatter(parameters, training_time, lowest_loss, c=accuracy, cmap='viridis'))
cbar.set_label('Accuracy (%)')

plt.title('4D Comparison')
plt.show()

plt.figure(figsize=(8, 6))
contour = plt.tricontourf(training_time, lowest_loss, accuracy, cmap='viridis')

plt.colorbar(contour, label='Accuracy (%)')

plt.xlabel('Model training time (minutes)')
plt.ylabel('Loss')

plt.title('2D Contour Plot of 3D Data')
plt.grid(True)
plt.show()